MDCPD:基于矩阵序列距离度量的数字生态变点检测  

MDCPD:Change Point Detection for Digital Ecosystem Based on Sequenced Matrices Distance Measurement

作　　者：朱业琪 刘明义 苏统华[1] 王忠杰[1] ZHU Ye-Qi;LIU Ming-Yi;SU Tong-Hua;WANG Zhong-Jie(Faculty of Computing,Harbin Institute of Technology,Harbin 150001)

机构地区：[1]哈尔滨工业大学计算学部,哈尔滨150001

出　　处：《计算机学报》2024年第10期2452-2465,共14页Chinese Journal of Computers

基　　金：国家重点研发计划资助项目(2021YFB3300700);国家自然科学基金资助项目(62372140,62277011)资助。

摘　　要：数字生态系统是一个分布式的、适应性的、开放的社会技术系统.随着大数据、物联网、云计算等技术的发展,数字生态的表现形式逐渐复杂多样,与人们的生活更加密切.数字生态受内外部激励自发性地持续演化,一些事件的发生可能会使数字生态的部分性质显著变化,偏离其正常的演化路径,进而导致生态伴随着异常不健康地发展,如果能够及时发现这些变化并定位引起变化的事件,然后加以人为干预,则可能将负面影响降到最低.动态复杂网络是一个辅助观测数字生态的有效工具,这使分析生态的演化情况成为可能,复杂网络分析领域中的变点检测是检测数字生态演化变点的主要技术手段之一.然而,目前已有的通用的变点检测方法未针对数字生态做出优化,忽视了数字生态的高度动态等特性,会导致这些方法可能无法在高度动态、持续变化的情况下检测变点,于是,已有方法在数字生态场景上的变点检测性能可能不佳.为解决上述问题,本文提出基于矩阵序列距离度量的数字生态变点检测方法(MDCPD),MDCPD是社区感知的,它从数字生态的社区视角观测数字生态的变化幅度,通过计算社区矩阵距离变化率在在连续时间动态网络建模的数字生态上高效地实现了变点检测,且变点检测和数字生态演化动因定位均是事件级别,能帮助生态的管理人员高效地进行干预和决策.为抵抗社区结构矩阵序列数据中的噪声对方法的影响,本文提出了矩阵干预策略,通过从数字生态中观测到的客观条件干预社区结构矩阵的数值,提高了社区结构矩阵序列对数字的生态结构表达能力.本文在基于合成数据的连续时间和离散时间两个场景的对比实验以及消融实验证明了MDCPD和矩阵干预策略的有效性,MDCPD的F1指标至多超过SOTA方法0.383,矩阵干预策略至多使MDCPD的F1指标提高了0.053.最后,本文在真实A digital ecosystem is a distributed,adaptive,and open social-technical system that possesses characteristics similar to natural ecosystems,such as self-organization,scalability,and sustainability.With the development of technologies such as big data,Internet of Things,and cloud computing,the concept of digital ecosystem has become increasingly complex and diverse,becoming more closely related to people's lives.Digital ecosystem evolves continuously due to the internal and external impacts.Some events may significantly change the properties of the digital ecosystem and make it deviates from its normal evolutionary path,thus causing the ecosystem unhealthily evolve with anomalies.If these changes can be detected and human intervention is carried out in time,the negative impact may be minimized.Digital ecosystem is observable through event flow,which naturally forms a network structure and makes it possible to analyze the evolution status of a digital ecosystem.Under this consideration,it is a mainstream to use the complex network to model a digital ecosystem,and change point detection in such field is one of the main techniques to detect evolution status of a digital ecosystem.However,few existing approach on change point detection are optimized for the characteristics of the digital ecosystems.They often overlook the dynamics property as they prefer discrete-time dynamic graph for modeling.This results in they are not able to support the detection of change points in a digital ecosystem under dynamic and continuous changing scenario,which brings the decrease of the performance in change point detection task for digital ecosystem.To address the problem,in this paper,we propose Change Point Detection for Digital Ecosystem Based on Sequenced Matrices Distance Measurement(MDCPD).MDCPD is a community-aware approach,which utilizes the community structure matrix sequence efficiently to assess the happened changes from community perspective.Therefore,MDCPD can detect change points in digital ecosystem under continuous-ti

关 键 词：数字生态 变点检测 动态网络 复杂网络分析 异常检测 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]